Predicting moisture content in kiln dried timbers using machine learning

Author:

Rahimi SohrabORCID,Avramidis Stavros

Funder

natural sciences and engineering research council of canada

Publisher

Springer Science and Business Media LLC

Subject

General Materials Science,Forestry

Reference68 articles.

1. Aghbashlo M (2015) Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying Technol 33(12):1397–1462

2. Anne JE (2000) Kiln tests with species and moisture content sorted, 116 mm square, hem-fir merch lumber: final report prepared for the stability work group. ZAIRAI Lumber Partnership Ltd, Vancouver

3. Avramidis S (2001) Evaluation of conventional and radio frequency vacuum drying and re-drying of Pacific Coast hemlock Hashira and Harakeke lumbers: final report prepared for the stability work group. ZAIRAI Lumber Partnership Ltd., Vancouver

4. Avramidis S, Iliadis L (2005) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci 37(4):682–690

5. Avramidis S, Wu H (2007) Artificial neural network and mathematical modeling comparative analysis of non isothermal diffusion of moisture in wood. Eur J Wood Prod 65(2):89–93

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